Robust H∞ filtering for Markovian jumping static neural networks with time-varying delays

被引：0
|
作者：
Zhao, Aodong [1 ]
Zhang, Nan [1 ]
Xi, Maolong [1 ]
Sun, Jun [2 ]
Dong, Meiyan [1 ]
机构：
[1] Wuxi Inst Technol, Sch Control Technol, Wuxi, Jiangsu, Peoples R China
[2] Jiangnan Univ, Sch IoT Internet Things, Jiangnan, Peoples R China
来源：
JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY | 2020年 / 14卷
关键词：
H-infinity filtering Markovian jumping; static neural networks; Wirtinger inequalities; STABILITY; SYSTEMS; DESIGN; BIAS;
D O I：
10.1177/1748302620931340
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The problem of robustH(infinity)filtering for Markovian jumping static neural networks with time-varying delays is considered in this paper. The effect of the activation function on the time delays is comprehensively considered. Based on Wirtinger inequality, a new inequality is quoted to solve the Lyapunov functions with the double-integral terms. Then, a less conservative result on the robustH(infinity)filtering is obtained, which guarantees the resulting error systems stochastically stable and satisfies a prescribedH(infinity)performance index. The effectiveness of the developed results is finally demonstrated by numerical examples.
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页数：11
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